Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.

中文翻译：

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快速，可靠和安全的无人机通信：全面调查

无人机安全性目前是研究人员和工业家之间讨论的主要话题。尽管无人机有多种应用，但是，如果没有预料到安全性挑战并且没有对架构进行必要的更改，那么即将到来的无人机应用将无法满足其实际目的。因此，在本文中，我们对安全性至关重要的无人机应用程序以及无人机通信中与安全性相关的挑战（例如DoS攻击，中间人攻击，De-Authentication攻击等）进行了详细的介绍。此外，作为解决方案架构的一部分，讨论了区块链，软件定义网络（SDN），机器学习和雾/边缘计算的使用，因为它们是最新兴的技术。无人机是高度资源受限的设备，因此无法在机上部署沉重的安全算法。区块链可用于加密存储发送到无人机或从无人机发送的所有数据，从而避免篡改和窃听。各种ML算法可用于检测网络中的恶意无人机并检测安全路由。此外，SDN技术可通过允许控制器密切检查数据流量来使无人机网络可靠，并且雾计算可用于使计算能力更接近无人机而不会使无人机过载。各种ML算法可用于检测网络中的恶意无人机并检测安全路由。此外，SDN技术可通过允许控制器密切检查数据流量来使无人机网络可靠，并且雾计算可用于使计算能力更接近无人机而不会使无人机过载。各种ML算法可用于检测网络中的恶意无人机并检测安全路由。此外，SDN技术可通过允许控制器密切检查数据流量来使无人机网络可靠，并且雾计算可用于使计算能力更接近无人机而不会使无人机过载。